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Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)
dc.contributor.author | Nemmaoui, Abderrahim | |
dc.contributor.author | Aguilar Torres, Manuel Ángel | |
dc.contributor.author | Aguilar Torres, Fernando José | |
dc.contributor.author | Novelli, Antonio | |
dc.contributor.author | García Lorca, Andrés | |
dc.date.accessioned | 2019-03-11T07:19:25Z | |
dc.date.available | 2019-03-11T07:19:25Z | |
dc.date.issued | 2018-11-30 | |
dc.identifier.uri | http://hdl.handle.net/10835/6417 | |
dc.description.abstract | A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary preclassification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (Fβ around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8. | es_ES |
dc.language.iso | en | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/ES/MINECO/AGL2014-56017-R/ES/Identificación basada en objetos de cultivos hortícolas bajo invernadero a partir de estéreo imágenes del satélite Worldview-3 y series temporales de Landsat 8/IBOCHBIEISWSTL | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Remote Sensing, Agriculture | es_ES |
dc.title | Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain) | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2072-4292/10/11/1751 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.doi | 10.3390/rs10111751 | |
dc.relation.projectID | AGL2014-56017-R | es_ES |